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What You Need to Know About Statistical Process Control (SPC)

Statistical Process Control, or SPC for short, has been around
since the 1920s although it didn't really gain widespread use in
industry until the 1980s. Many people are immediately turned
off of SPC just because it has "statistical" in its name.
However, by simply understanding a few basic concepts of variation
(why things are not ALWAYS made exactly the same) you will be able
to leverage the concepts of SPC to monitor and control your
manufacturing processes.

Variation: The Heart of SPC

Variation exists in everything

When we’re manufacturing products with customers that expect
and demand high quality and consistency in our goods, variation
can become a big problem.

Too much variation leads to rework, scrap, or customer
problems.

A perfect process would be one with no variation. They don’t
exist.

As the variation in our processes is reduced, the output of
our processes will be improved.

That’s our goal with SPC-to reduce the variation in our
processes and then monitor the process to make sure the
variation doesn’t increase.

Our Statistical Process Control (SPC) Resource Center has lots of
helpful information for people just learning about SPC as well as
some interesting information that folks who have been using SPC for
years should find interesting.

Which is better? In-Spec or In-Control?

Is there a difference between being in-control and in-spec? Yes
there is, and it is a big difference. You can be in-spec but not
in-control. And you can be in-control but not in-spec.

So, if you can have only one, which is better - being in-spec or
in-control? Some may say “in-spec of course.” But unless the spec is
very generous and forgiving, you will always be better off with a
process that is in-control, but not completely in-spec. Obviously
neither is the ideal situation - but there is a fundamental problem
with a process that is in-spec, but not in-control. The problem is
that you never know from one minute to the next if it will go
out-of-spec. This means that you must inspect every part or product
that is made - otherwise, how will you know that the product is
still in-spec?

So, what is the advantage of a process that is in-control but not
in-spec? Let's assume that at least some of the product being made
by this in-control process is in-spec. Given that some of the
process output is in-spec and the process is in-control, we can
actually statistically calculate how much of the product will be
out-of-spec.

Of course, the goal is to have a process that is both in-control
and in-spec - that is what we call a capable and centered process.
What if you have a process that is in-control, but not in-spec? By
using SPC, root cause analysis, and some common sense, an in-control
process can usually be brought in-spec.

How do you know if your processes are in-control? A good first
step to take is to create a histogram of your data and then analyze
the pattern of variation to identify possible reasons for the
pattern. A control chart is really just an extension of a histogram.
Not only does it show you the pattern of variation, but it plots
that pattern over time.